SNDocRank: A social network-based video search ranking framework

Liang Gou, Hung Hsuan Chen, Jung Hyun Kim, Xiaolong Zhang, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

13 Scopus citations

Abstract

Multimedia ranking algorithms are usually user-neutral and measure the importance and relevance of documents by only using the visual contents and meta-data. However, users' interests and preferences are often diverse, and may demand different results even with the same queries. How can we integrate user interests in ranking algorithms to improve search results? Here, we introduce Social Network Document Rank (SNDocRank), a new ranking framework that considers a searcher's social network, and apply it to video search. SNDocRank integrates traditional tf-idf ranking with our Multi-level Actor Similarity (MAS) algorithm, which measures the similarity between social networks of a searcher and document owners. Results from our evaluation study with a social network and video data from YouTube show that SNDocRank offers search results more relevant to user's interests than other traditional ranking methods.

Original languageEnglish (US)
Title of host publicationMIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval
Pages367-376
Number of pages10
DOIs
StatePublished - May 21 2010
Event2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010 - Philadelphia, PA, United States
Duration: Mar 29 2010Mar 31 2010

Publication series

NameMIR 2010 - Proceedings of the 2010 ACM SIGMM International Conference on Multimedia Information Retrieval

Other

Other2010 ACM SIGMM International Conference on Multimedia Information Retrieval, MIR 2010
CountryUnited States
CityPhiladelphia, PA
Period3/29/103/31/10

All Science Journal Classification (ASJC) codes

  • Computer Graphics and Computer-Aided Design
  • Information Systems

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